Observed El Niño-La Niña Asymmetry in a Linear Model

Cristian Martinez-Villalobos, Matthew Newman, Daniel J. Vimont, Cécile Penland, J. David Neelin

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Previous studies indicate an asymmetry in the amplitude and persistence of El Niño (EN) and La Niña (LN) events. We show that this observed EN-LN asymmetry can be captured with a linear model driven by correlated additive and multiplicative (CAM) noise, without resorting to a deterministic nonlinear model. The model is derived from 1-month lag statistics taken from monthly sea surface temperature (SST) data sets spanning the twentieth century, in an extension of an empirical-dynamical technique called Linear Inverse Modeling. Our results suggest that noise amplitudes tend to be stronger for EN compared to LN events, which is sufficient to generate asymmetry in amplitude and also produces more persistent LN events on average. These results establish a null hypothesis for EN-LN asymmetry and suggest that strong EN events may not be more predictable that what can be accounted for by a multivariate linear system driven by CAM noise.

Original languageEnglish
Pages (from-to)9909-9919
Number of pages11
JournalGeophysical Research Letters
Volume46
Issue number16
DOIs
StatePublished - 28 Aug 2019

Keywords

  • ENSO
  • LIM
  • nonlinear
  • predictability
  • stochastic
  • tropical Pacific

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